Answer "what will next month cost?"
with a number you can defend
ML spend is hard to project because it mixes steady inference baselines, bursty training, and usage-based LLM costs into one line item. MLCostIntel separates those components and forecasts each on its own trend, which is what makes the total believable.
Why ML budgets keep missing
Three cost behaviors, one blended number
Steady inference baselines, bursty training campaigns, and usage-scaling LLM APIs move on completely different curves. Forecasting the blended total is why last quarter's projection missed by 40%.
Growth changes the math mid-quarter
A feature launch doubles inference traffic. A new model doubles token spend. If you're only tracking the total, you can't tell planned growth from a problem until the quarter is over.
A company-wide budget has no owner
One aggregate ML budget can't be enforced because no single team is responsible for it. Budgets start working when each team has its own number and can see it moving, and that requires allocation to exist first.
Finance can't sanity-check the inputs
A forecast built in an engineering spreadsheet is opaque to finance. One built from the invoice is two months stale by the time anyone reviews it. Board questions tend to find the weakness in both.
How MLCostIntel makes ML spend forecastable
Attribution comes first: once spend is classified by workload and team, each component can be projected on its own trend — and the forecast becomes a set of line items anyone can interrogate.
- ✓ Component-level trends — inference run-rates, training campaign patterns, and token usage curves projected separately
- ✓ Team and project budgets — tracked where accountability lives, with mid-month drift alerts
- ✓ Forecast vs. actual — every month closes the loop so projections improve instead of resetting
- ✓ Unit economics — cost per model, per prediction, and per request for pricing and margin decisions
- ✓ Executive-ready reporting — board-deck numbers without finance learning an engineering tool
- ✓ Daily-refreshed inputs — forecasts built on current usage, not last month's invoice
What teams get out of it
Forecast transparency
Projections finance can interrogate component by component
Budget drift alerts
Course-correct while it's cheap, not after the invoice
Budget ownership
Each team tracks its own number against its own trend
Economics for pricing
Cost per prediction and per request to defend AI feature margins